FIA NLS Models: Biomass Growth vs. Biomass – Data Subsets
JAH
2023-07-12
Background
This analysis document compliments FIA NLS Models: Biomass Growth vs. Biomass. All of the background information from that document applies to these analyses, which are extensions to them. The difference between that document and this analysis is the use of different data subsets.
Here, we fit the models using: 1) a temporally-balanced dataset, where we take the first and most-recent plot record for all plots in the dataset, 2) a temporally-balanced dataset (same as #1), but which excludes plot locations which have experienced harvest (at any point over the study interval 2000-2022)
Below the model fitting procedure is implemented by ecoprovince:
Temporally-balancing the biomass growth data set
Lets look at some quick attributes of the dataset
- The data set has 113960 observations, comprised of 57686 plots.
- The frequency of growth measurements among plots is as follows (n=1 through 5): 25573, 13681, 12815, 5505, 112.
- Thus 55.67% of plots have at least two growth measurements.
Analysis 1: Temporally-balanced analysis
211 - Northeastern Mixed Forest
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 4799 4403.7
## 2 4798 4218.7 1 185.03 210.43 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 18635.35
## 2 2 18431.23##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 -
## alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 0.11645 0.16803 0.693 0.488
## alpha 0.63059 0.04079 15.460 <2e-16 ***
## A 3.59795 0.12729 28.266 <2e-16 ***
## k 7.38570 0.78455 9.414 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9377 on 4798 degrees of freedom
##
## Number of iterations to convergence: 7
## Achieved convergence tolerance: 3.026e-06
## (36 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
model selection 2
## Error in nls(get(paste("fg_", Mod.Sel1, "a", sep = "")), data = G_211, :
## object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "b", sep = "")), data = G_211, :
## object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "c", sep = "")), data = G_211, :
## object 'ge.fit' not found
## model AIC
## 1 2 18431.23
## 2 2a NA
## 3 2b NA
## 4 2c NA##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 -
## alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 0.11645 0.16803 0.693 0.488
## alpha 0.63059 0.04079 15.460 <2e-16 ***
## A 3.59795 0.12729 28.266 <2e-16 ***
## k 7.38570 0.78455 9.414 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9377 on 4798 degrees of freedom
##
## Number of iterations to convergence: 7
## Achieved convergence tolerance: 3.026e-06
## (36 observations deleted due to missingness)summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
predict and plot
## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.## Warning: Removed 19 rows containing missing values (`geom_point()`).## Warning: Removed 1050 rows containing missing values (`geom_line()`).
plotting 2
212 - Laurentian Mixed Forest
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 9768 9688.5
## 2 9767 9061.5 1 627.04 675.86 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 35804.93
## 2 2 35153.16##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 -
## alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 1.23636 0.21162 5.842 5.31e-09 ***
## alpha 0.81205 0.02856 28.435 < 2e-16 ***
## A 2.54337 0.09180 27.706 < 2e-16 ***
## k 10.17903 0.59487 17.111 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9632 on 9767 degrees of freedom
##
## Number of iterations to convergence: 7
## Achieved convergence tolerance: 7.232e-06
## (3191 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
model selection 2
## Error in nls(get(paste("fg_", Mod.Sel1, "a", sep = "")), data = G_212, :
## object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "b", sep = "")), data = G_212, :
## object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "c", sep = "")), data = G_212, :
## object 'ge.fit' not found
## model AIC
## 1 2 35153.16
## 2 2a NA
## 3 2b NA
## 4 2c NA##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 -
## alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 1.23636 0.21162 5.842 5.31e-09 ***
## alpha 0.81205 0.02856 28.435 < 2e-16 ***
## A 2.54337 0.09180 27.706 < 2e-16 ***
## k 10.17903 0.59487 17.111 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9632 on 9767 degrees of freedom
##
## Number of iterations to convergence: 7
## Achieved convergence tolerance: 7.232e-06
## (3191 observations deleted due to missingness)summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
predict and plot
## Warning: Removed 1591 rows containing missing values (`geom_point()`).## Warning: Removed 1031 rows containing missing values (`geom_line()`).
plotting 2
221 - Eastern Broadleaf Forest
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 5407 7012.9
## 2 5406 6755.7 1 257.25 205.85 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 23593.91
## 2 2 23393.72##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 -
## alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau -0.59298 0.14354 -4.131 3.66e-05 ***
## alpha 0.69426 0.04558 15.231 < 2e-16 ***
## A 4.89744 0.18018 27.181 < 2e-16 ***
## k 14.72783 1.83947 8.007 1.43e-15 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.118 on 5406 degrees of freedom
##
## Number of iterations to convergence: 7
## Achieved convergence tolerance: 3.879e-06
## (36 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
model selection 2
## Error in nls(get(paste("fg_", Mod.Sel1, "a", sep = "")), data = G_221, :
## object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "b", sep = "")), data = G_221, :
## object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "c", sep = "")), data = G_221, :
## object 'ge.fit' not found
## model AIC
## 1 2 23393.72
## 2 2a NA
## 3 2b NA
## 4 2c NA##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 -
## alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau -0.59298 0.14354 -4.131 3.66e-05 ***
## alpha 0.69426 0.04558 15.231 < 2e-16 ***
## A 4.89744 0.18018 27.181 < 2e-16 ***
## k 14.72783 1.83947 8.007 1.43e-15 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.118 on 5406 degrees of freedom
##
## Number of iterations to convergence: 7
## Achieved convergence tolerance: 3.879e-06
## (36 observations deleted due to missingness)summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
predict and plot
## Warning: Removed 20 rows containing missing values (`geom_point()`).## Warning: Removed 1036 rows containing missing values (`geom_line()`).
plotting 2
222 - Midwest Broadleaf Forest
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 2738 2904.7
## 2 2737 2699.1 1 205.61 208.5 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 10921.69
## 2 2 10722.46##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 -
## alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 0.20704 0.27193 0.761 0.446
## alpha 0.84073 0.05282 15.917 <2e-16 ***
## A 4.20770 0.24329 17.295 <2e-16 ***
## k 20.43092 2.04843 9.974 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.993 on 2737 degrees of freedom
##
## Number of iterations to convergence: 9
## Achieved convergence tolerance: 3.614e-06
## (811 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
model selection 2
## Error in nls(get(paste("fg_", Mod.Sel1, "a", sep = "")), data = G_222, :
## object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "b", sep = "")), data = G_222, :
## object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "c", sep = "")), data = G_222, :
## object 'ge.fit' not found
## model AIC
## 1 2 10722.46
## 2 2a NA
## 3 2b NA
## 4 2c NA##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 -
## alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 0.20704 0.27193 0.761 0.446
## alpha 0.84073 0.05282 15.917 <2e-16 ***
## A 4.20770 0.24329 17.295 <2e-16 ***
## k 20.43092 2.04843 9.974 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.993 on 2737 degrees of freedom
##
## Number of iterations to convergence: 9
## Achieved convergence tolerance: 3.614e-06
## (811 observations deleted due to missingness)summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
predict and plot
## Warning: Removed 419 rows containing missing values (`geom_point()`).## Warning: Removed 1108 rows containing missing values (`geom_line()`).
plotting 2
223 - Central Interior Broadleaf Forest
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 5257 6464.4
## 2 5256 6291.4 1 172.96 144.49 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 21832.82
## 2 2 21692.17##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 -
## alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau -0.83553 0.12268 -6.811 1.08e-11 ***
## alpha 0.64175 0.05008 12.815 < 2e-16 ***
## A 5.20077 0.20318 25.596 < 2e-16 ***
## k 30.29086 3.15408 9.604 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.094 on 5256 degrees of freedom
##
## Number of iterations to convergence: 12
## Achieved convergence tolerance: 9.644e-06
## (1128 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
model selection 2
## Error in nls(get(paste("fg_", Mod.Sel1, "a", sep = "")), data = G_223, :
## object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "b", sep = "")), data = G_223, :
## object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "c", sep = "")), data = G_223, :
## object 'ge.fit' not found
## model AIC
## 1 2 21692.17
## 2 2a NA
## 3 2b NA
## 4 2c NA##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 -
## alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau -0.83553 0.12268 -6.811 1.08e-11 ***
## alpha 0.64175 0.05008 12.815 < 2e-16 ***
## A 5.20077 0.20318 25.596 < 2e-16 ***
## k 30.29086 3.15408 9.604 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.094 on 5256 degrees of freedom
##
## Number of iterations to convergence: 12
## Achieved convergence tolerance: 9.644e-06
## (1128 observations deleted due to missingness)summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
predict and plot
## Warning: Removed 579 rows containing missing values (`geom_point()`).## Warning: Removed 1145 rows containing missing values (`geom_line()`).
plotting 2
231 - Southeastern Mixed Forest
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 7654 15414
## 2 7653 13881 1 1532.7 845.01 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 38274.53
## 2 2 37474.57##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 -
## alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 1.56725 0.21078 7.436 1.15e-13 ***
## alpha 0.87196 0.02725 31.999 < 2e-16 ***
## A 3.83483 0.12639 30.340 < 2e-16 ***
## k 1.35721 0.22062 6.152 8.05e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.347 on 7653 degrees of freedom
##
## Number of iterations to convergence: 10
## Achieved convergence tolerance: 7.675e-06
## (133 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
model selection 2
## Error in nls(get(paste("fg_", Mod.Sel1, "a", sep = "")), data = G_231, :
## object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "b", sep = "")), data = G_231, :
## object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "c", sep = "")), data = G_231, :
## object 'ge.fit' not found
## model AIC
## 1 2 37474.57
## 2 2a NA
## 3 2b NA
## 4 2c NA##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 -
## alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 1.56725 0.21078 7.436 1.15e-13 ***
## alpha 0.87196 0.02725 31.999 < 2e-16 ***
## A 3.83483 0.12639 30.340 < 2e-16 ***
## k 1.35721 0.22062 6.152 8.05e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.347 on 7653 degrees of freedom
##
## Number of iterations to convergence: 10
## Achieved convergence tolerance: 7.675e-06
## (133 observations deleted due to missingness)summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
predict and plot
## Warning: Removed 67 rows containing missing values (`geom_point()`).## Warning: Removed 1017 rows containing missing values (`geom_line()`).
plotting 2
232 - Outer Coastal Plain Mixed Forest
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 7765 19227
## 2 7764 17588 1 1639.3 723.69 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 39432.03
## 2 2 38741.76##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 -
## alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 1.12531 0.21903 5.138 2.85e-07 ***
## alpha 0.86821 0.02892 30.018 < 2e-16 ***
## A 4.01181 0.15286 26.245 < 2e-16 ***
## k 5.60838 0.54424 10.305 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.505 on 7764 degrees of freedom
##
## Number of iterations to convergence: 7
## Achieved convergence tolerance: 9.58e-06
## (172 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
model selection 2
## Error in nls(get(paste("fg_", Mod.Sel1, "a", sep = "")), data = G_232, :
## object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "b", sep = "")), data = G_232, :
## object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "c", sep = "")), data = G_232, :
## object 'ge.fit' not found
## model AIC
## 1 2 38741.76
## 2 2a NA
## 3 2b NA
## 4 2c NA##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 -
## alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 1.12531 0.21903 5.138 2.85e-07 ***
## alpha 0.86821 0.02892 30.018 < 2e-16 ***
## A 4.01181 0.15286 26.245 < 2e-16 ***
## k 5.60838 0.54424 10.305 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.505 on 7764 degrees of freedom
##
## Number of iterations to convergence: 7
## Achieved convergence tolerance: 9.58e-06
## (172 observations deleted due to missingness)summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
predict and plot
## Warning: Removed 91 rows containing missing values (`geom_point()`).## Warning: Removed 981 rows containing missing values (`geom_line()`).
plotting 2
234 - Lower Mississippi Riverine Forest
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 798 1787.7
## 2 797 1679.2 1 108.44 51.471 1.663e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 4024.928
## 2 2 3976.801##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 -
## alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 0.53177 0.68863 0.772 0.440
## alpha 0.78767 0.09903 7.954 6.18e-15 ***
## A 4.11911 0.55948 7.362 4.51e-13 ***
## k 2.01994 1.23877 1.631 0.103
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.452 on 797 degrees of freedom
##
## Number of iterations to convergence: 7
## Achieved convergence tolerance: 5.905e-06
## (29 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
model selection 2
## Error in nls(get(paste("fg_", Mod.Sel1, "a", sep = "")), data = G_234, :
## object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "b", sep = "")), data = G_234, :
## object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "c", sep = "")), data = G_234, :
## object 'ge.fit' not found
## model AIC
## 1 2 3976.801
## 2 2a NA
## 3 2b NA
## 4 2c NA##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 -
## alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 0.53177 0.68863 0.772 0.440
## alpha 0.78767 0.09903 7.954 6.18e-15 ***
## A 4.11911 0.55948 7.362 4.51e-13 ***
## k 2.01994 1.23877 1.631 0.103
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.452 on 797 degrees of freedom
##
## Number of iterations to convergence: 7
## Achieved convergence tolerance: 5.905e-06
## (29 observations deleted due to missingness)summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
predict and plot
## Warning: Removed 10 rows containing missing values (`geom_point()`).## Warning: Removed 1077 rows containing missing values (`geom_line()`).
plotting 2
242 - Pacific Lowland Mixed Forest
model selection 1
## Error in if (any(nEQ <- vNms != make.names(vNms))) vNms[nEQ] <- paste0("`", :
## missing value where TRUE/FALSE needed
## Error in if (any(nEQ <- vNms != make.names(vNms))) vNms[nEQ] <- paste0("`", :
## missing value where TRUE/FALSE needed
## model AIC
## 1 1 NA
## 2 2 NA## Warning in min(AIC1_242$AIC, na.rm = T): no non-missing arguments to min;
## returning Inf## Error in h(simpleError(msg, call)) :
## error in evaluating the argument 'object' in selecting a method for function 'summary': object 'nls_242.' not foundsummary
- simple model: does not fit
- alpha model: does not fit
model selection 2
summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
predict and plot
## [1] "cannot plot data with prediction"plotting 2
251 - Prairie Parkland (Temperate)
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 977 1385.2
## 2 976 1373.9 1 11.286 8.0178 0.004727 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 4120.597
## 2 2 4114.579##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 -
## alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 0.01826 0.53851 0.034 0.97296
## alpha 0.45506 0.15286 2.977 0.00298 **
## A 3.31919 0.39912 8.316 3.03e-16 ***
## k 10.40557 3.41241 3.049 0.00236 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.186 on 976 degrees of freedom
##
## Number of iterations to convergence: 12
## Achieved convergence tolerance: 6.762e-06
## (412 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
model selection 2
## Error in nls(get(paste("fg_", Mod.Sel1, "a", sep = "")), data = G_251, :
## object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "b", sep = "")), data = G_251, :
## object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "c", sep = "")), data = G_251, :
## object 'ge.fit' not found
## model AIC
## 1 2 4114.579
## 2 2a NA
## 3 2b NA
## 4 2c NA##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 -
## alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 0.01826 0.53851 0.034 0.97296
## alpha 0.45506 0.15286 2.977 0.00298 **
## A 3.31919 0.39912 8.316 3.03e-16 ***
## k 10.40557 3.41241 3.049 0.00236 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.186 on 976 degrees of freedom
##
## Number of iterations to convergence: 12
## Achieved convergence tolerance: 6.762e-06
## (412 observations deleted due to missingness)summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
predict and plot
## Warning: Removed 224 rows containing missing values (`geom_point()`).## Warning: Removed 1176 rows containing missing values (`geom_line()`).
plotting 2
255 - Prairie Parkland (Subtropical)
model selection 1
## Error in nls(fg_1, data = G_255, start = c(tau = tau.start, A = A.start, :
## number of iterations exceeded maximum of 50
## Error in nls(fg_2, data = G_255, start = c(tau = tau.start, alpha = alpha.start, :
## number of iterations exceeded maximum of 50
## model AIC
## 1 1 NA
## 2 2 NA## Warning in min(AIC1_255$AIC, na.rm = T): no non-missing arguments to min;
## returning Inf## Error in h(simpleError(msg, call)) :
## error in evaluating the argument 'object' in selecting a method for function 'summary': object 'nls_255.' not foundsummary
- simple model: does not fit
- alpha model: does not fit
model selection 2
summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
predict and plot
## [1] "cannot plot data with prediction"plotting 2
261 - California Coastal Chaparral Forest and Shrub
model selection 1
summary
- simple model: does not fit
- alpha model: does not fit
model selection 2
summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
predict and plot
## [1] "cannot plot data with prediction"plotting 2
262 - California Dry Steppe
model selection 1
summary
- simple model: does not fit
- alpha model: does not fit
model selection 2
summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
predict and plot
## [1] "cannot plot data with prediction"plotting 2
263 - California Coastal Steppe - Mixed Forest and Redwood Forest
model selection 1
summary
- simple model: does not fit
- alpha model: does not fit
model selection 2
summary
add p model: does not fit
add s model: does not fit
add s+p model: does not fit
note: model fit, but fit was funky due to data being sparse
predict and plot
## [1] "cannot plot data with prediction"plotting 2
313 - Colorado Plateau Semi-Desert
model selection 1
## Error in if (any(nEQ <- vNms != make.names(vNms))) vNms[nEQ] <- paste0("`", :
## missing value where TRUE/FALSE needed
## Error in if (any(nEQ <- vNms != make.names(vNms))) vNms[nEQ] <- paste0("`", :
## missing value where TRUE/FALSE needed
## model AIC
## 1 1 NA
## 2 2 NA## Warning in min(AIC1_313$AIC, na.rm = T): no non-missing arguments to min;
## returning Inf## Error in h(simpleError(msg, call)) :
## error in evaluating the argument 'object' in selecting a method for function 'summary': object 'nls_313.' not foundsummary
- simple model: does not fit
- alpha model: does not fit
model selection 2
summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
predict and plot
## [1] "cannot plot data with prediction"plotting 2
315 - Southwest Plateau and Plains Dry Steppe and Shrub
model selection 1
summary
- simple model: does not fit
- alpha model: does not fit
model selection 2
summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
predict and plot
## [1] "cannot plot data with prediction"plotting 2
321 - Chihuahuan Semi-Desert
model selection 1
summary
- simple model: does not fit
- alpha model: does not fit
model selection 2
summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
predict and plot
## [1] "cannot plot data with prediction"plotting 2
322 - American Semidesert and Desert
model selection 1
summary
- simple model: does not fit
- alpha model: does not fit
model selection 2
summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
predict and plot
## [1] "cannot plot data with prediction"plotting 2
331 - Great Plains/Palouse Dry Steppe
model selection 1
## Error in nls(fg_1, data = G_331, start = c(tau = tau.start, A = A.start, :
## number of iterations exceeded maximum of 50
## Error in nls(fg_2, data = G_331, start = c(tau = tau.start, alpha = alpha.start, :
## number of iterations exceeded maximum of 50
## model AIC
## 1 1 NA
## 2 2 NA## Warning in min(AIC1_331$AIC, na.rm = T): no non-missing arguments to min;
## returning Inf## Error in h(simpleError(msg, call)) :
## error in evaluating the argument 'object' in selecting a method for function 'summary': object 'nls_331.' not foundsummary
- simple model: does not fit
- alpha model: does not fit
model selection 2
summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
predict and plot
## [1] "cannot plot data with prediction"plotting 2
332 - Great Plains Steppe
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 136 120.26
## 2 135 117.52 1 2.7361 3.143 0.07851 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 459.1608
## 2 2 457.9617##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 -
## alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 1.5868 2.5256 0.628 0.5309
## alpha 0.5938 0.3038 1.955 0.0527 .
## A 2.9848 1.3104 2.278 0.0243 *
## k 58.3210 24.2463 2.405 0.0175 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.933 on 135 degrees of freedom
##
## Number of iterations to convergence: 6
## Achieved convergence tolerance: 1.529e-06
## (15 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
model selection 2
## Error in nls(get(paste("fg_", Mod.Sel1, "a", sep = "")), data = G_332, :
## object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "b", sep = "")), data = G_332, :
## object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "c", sep = "")), data = G_332, :
## object 'ge.fit' not found
## model AIC
## 1 2 457.9617
## 2 2a NA
## 3 2b NA
## 4 2c NA##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 -
## alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 1.5868 2.5256 0.628 0.5309
## alpha 0.5938 0.3038 1.955 0.0527 .
## A 2.9848 1.3104 2.278 0.0243 *
## k 58.3210 24.2463 2.405 0.0175 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.933 on 135 degrees of freedom
##
## Number of iterations to convergence: 6
## Achieved convergence tolerance: 1.529e-06
## (15 observations deleted due to missingness)summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
predict and plot
## Warning: Removed 7 rows containing missing values (`geom_point()`).## Warning: Removed 1140 rows containing missing values (`geom_line()`).
plotting 2
341 - Intermountain Semi-desert & Desert
model selection 1
summary
- simple model: does not fit
- alpha model: does not fit
model selection 2
summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
predict and plot
## [1] "cannot plot data with prediction"plotting 2
342 - Intermountain Semi-Desert
model selection 1
## Error in nls(fg_1, data = G_342, start = c(tau = tau.start, A = A.start, :
## number of iterations exceeded maximum of 50
## Error in nls(fg_2, data = G_342, start = c(tau = tau.start, alpha = alpha.start, :
## number of iterations exceeded maximum of 50
## model AIC
## 1 1 NA
## 2 2 NA## Warning in min(AIC1_342$AIC, na.rm = T): no non-missing arguments to min;
## returning Inf## Error in h(simpleError(msg, call)) :
## error in evaluating the argument 'object' in selecting a method for function 'summary': object 'nls_342.' not foundsummary
- simple model: does not fit
- alpha model: does not fit
model selection 2
summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
predict and plot
## [1] "cannot plot data with prediction"plotting 2
411 - Everglades
model selection 1
summary
- simple model: does not fit
- alpha model: does not fit
model selection 2
summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
predict and plot
## [1] "cannot plot data with prediction"plotting 2
M211 - Adirondack-New England Mixed forest - Coniferous Forest - Alpine Meadow
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 5091 4294.6
## 2 5090 4058.8 1 235.79 295.69 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 19299.92
## 2 2 19014.27##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 -
## alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 0.90463 0.22963 3.939 8.28e-05 ***
## alpha 0.64109 0.03478 18.435 < 2e-16 ***
## A 2.91302 0.12155 23.965 < 2e-16 ***
## k 2.85528 0.48360 5.904 3.77e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.893 on 5090 degrees of freedom
##
## Number of iterations to convergence: 7
## Achieved convergence tolerance: 3.974e-06
## (14 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
model selection 2
## Error in nls(get(paste("fg_", Mod.Sel1, "a", sep = "")), data = G_M211, :
## object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "b", sep = "")), data = G_M211, :
## object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "c", sep = "")), data = G_M211, :
## object 'ge.fit' not found
## model AIC
## 1 2 19014.27
## 2 2a NA
## 3 2b NA
## 4 2c NA##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 -
## alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 0.90463 0.22963 3.939 8.28e-05 ***
## alpha 0.64109 0.03478 18.435 < 2e-16 ***
## A 2.91302 0.12155 23.965 < 2e-16 ***
## k 2.85528 0.48360 5.904 3.77e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.893 on 5090 degrees of freedom
##
## Number of iterations to convergence: 7
## Achieved convergence tolerance: 3.974e-06
## (14 observations deleted due to missingness)summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
predict and plot
## Warning: Removed 6 rows containing missing values (`geom_point()`).## Warning: Removed 1108 rows containing missing values (`geom_line()`).
plotting 2
M221 - Eastern Broadleaf Forest
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 5156 9837.2
## 2 5155 9614.7 1 222.49 119.29 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 24619.42
## 2 2 24503.40##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 -
## alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 0.45747 0.22378 2.044 0.041 *
## alpha 0.80043 0.06929 11.552 < 2e-16 ***
## A 3.80503 0.17110 22.238 < 2e-16 ***
## k 8.31974 1.87264 4.443 9.07e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.366 on 5155 degrees of freedom
##
## Number of iterations to convergence: 7
## Achieved convergence tolerance: 8.2e-06
## (27 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
model selection 2
## Error in nls(get(paste("fg_", Mod.Sel1, "a", sep = "")), data = G_M221, :
## object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "b", sep = "")), data = G_M221, :
## object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "c", sep = "")), data = G_M221, :
## object 'ge.fit' not found
## model AIC
## 1 2 24503.4
## 2 2a NA
## 3 2b NA
## 4 2c NA##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 -
## alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 0.45747 0.22378 2.044 0.041 *
## alpha 0.80043 0.06929 11.552 < 2e-16 ***
## A 3.80503 0.17110 22.238 < 2e-16 ***
## k 8.31974 1.87264 4.443 9.07e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.366 on 5155 degrees of freedom
##
## Number of iterations to convergence: 7
## Achieved convergence tolerance: 8.2e-06
## (27 observations deleted due to missingness)summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
predict and plot
## Warning: Removed 15 rows containing missing values (`geom_point()`).## Warning: Removed 982 rows containing missing values (`geom_line()`).
plotting 2
M223 - Ozark Broadleaf Forest Meadow
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 596 960.49
## 2 595 931.66 1 28.823 18.408 2.081e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 2564.160
## 2 2 2547.909##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 -
## alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 3.0417 1.6720 1.819 0.0694 .
## alpha 0.9604 0.2071 4.638 4.32e-06 ***
## A 1.9026 0.4356 4.368 1.48e-05 ***
## k 9.2719 6.2331 1.488 0.1374
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.251 on 595 degrees of freedom
##
## Number of iterations to convergence: 12
## Achieved convergence tolerance: 6.676e-06
## (3 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
model selection 2
## Error in nls(get(paste("fg_", Mod.Sel1, "a", sep = "")), data = G_M223, :
## object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "b", sep = "")), data = G_M223, :
## object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "c", sep = "")), data = G_M223, :
## object 'ge.fit' not found
## model AIC
## 1 2 2547.909
## 2 2a NA
## 3 2b NA
## 4 2c NA##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 -
## alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 3.0417 1.6720 1.819 0.0694 .
## alpha 0.9604 0.2071 4.638 4.32e-06 ***
## A 1.9026 0.4356 4.368 1.48e-05 ***
## k 9.2719 6.2331 1.488 0.1374
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.251 on 595 degrees of freedom
##
## Number of iterations to convergence: 12
## Achieved convergence tolerance: 6.676e-06
## (3 observations deleted due to missingness)summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
predict and plot
## Warning: Removed 2 rows containing missing values (`geom_point()`).## Warning: Removed 1175 rows containing missing values (`geom_line()`).
plotting 2
M231 - Ouachita Mixed Forest
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 670 950.61
## 2 669 910.08 1 40.524 29.789 6.792e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 2806.978
## 2 2 2779.659##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 -
## alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 7.7044 4.5986 1.675 0.09433 .
## alpha 0.9156 0.1561 5.864 7.1e-09 ***
## A 1.1221 0.4247 2.642 0.00842 **
## k 3.4481 2.0307 1.698 0.08997 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.166 on 669 degrees of freedom
##
## Number of iterations to convergence: 16
## Achieved convergence tolerance: 7.883e-06
## (7 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
model selection 2
## Error in nls(get(paste("fg_", Mod.Sel1, "a", sep = "")), data = G_M231, :
## object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "b", sep = "")), data = G_M231, :
## object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "c", sep = "")), data = G_M231, :
## object 'ge.fit' not found
## model AIC
## 1 2 2779.659
## 2 2a NA
## 3 2b NA
## 4 2c NA##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 -
## alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 7.7044 4.5986 1.675 0.09433 .
## alpha 0.9156 0.1561 5.864 7.1e-09 ***
## A 1.1221 0.4247 2.642 0.00842 **
## k 3.4481 2.0307 1.698 0.08997 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.166 on 669 degrees of freedom
##
## Number of iterations to convergence: 16
## Achieved convergence tolerance: 7.883e-06
## (7 observations deleted due to missingness)summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
predict and plot
## Warning: Removed 6 rows containing missing values (`geom_point()`).## Warning: Removed 1218 rows containing missing values (`geom_line()`).
plotting 2
M242 - Cascade Mixed Forest
model selection 1
summary
- simple model: does not fit
- alpha model: does not fit
model selection 2
summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
predict and plot
## [1] "cannot plot data with prediction"plotting 2
M261 - Sierran Steppe - Mixed Forest - Coniferous Forest - Alpine Meadow
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 160 178.65
## 2 159 174.20 1 4.4514 4.0629 0.04552 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 554.3784
## 2 2 552.2656##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 -
## alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau -1.1541 3.1257 -0.369 0.7125
## alpha 0.7703 0.3514 2.192 0.0298 *
## A 9.1205 9.6033 0.950 0.3437
## k 216.5106 149.5716 1.448 0.1497
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.047 on 159 degrees of freedom
##
## Number of iterations to convergence: 7
## Achieved convergence tolerance: 1.14e-06
## (167 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
model selection 2
## Error in nls(get(paste("fg_", Mod.Sel1, "a", sep = "")), data = G_M261, :
## object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "b", sep = "")), data = G_M261, :
## object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "c", sep = "")), data = G_M261, :
## object 'ge.fit' not found
## model AIC
## 1 2 552.2656
## 2 2a NA
## 3 2b NA
## 4 2c NA##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 -
## alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau -1.1541 3.1257 -0.369 0.7125
## alpha 0.7703 0.3514 2.192 0.0298 *
## A 9.1205 9.6033 0.950 0.3437
## k 216.5106 149.5716 1.448 0.1497
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.047 on 159 degrees of freedom
##
## Number of iterations to convergence: 7
## Achieved convergence tolerance: 1.14e-06
## (167 observations deleted due to missingness)summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
##
## ------
## Shapiro-Wilk normality test
##
## data: stdres
## W = 0.9628, p-value = 0.0002337
##
##
## ------##
## Runs Test
##
## data: as.factor(run)
## Standard Normal = 0.53804, p-value = 0.5906
## alternative hypothesis: two.sidedpredict and plot
## Warning: Removed 83 rows containing missing values (`geom_point()`).## Warning: Removed 1274 rows containing missing values (`geom_line()`).
plotting 2
M262 - Califormia Coastal Range = Coniferous Forest - Open woodland Shrub Meadow
model selection 1
summary
- simple model: does not fit
- alpha model: does not fit
model selection 2
summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
predict and plot
## [1] "cannot plot data with prediction"plotting 2
M313 - Arizona-New Mexico Mountains Semi-Desert - Open Woodland - Coniferous Forest - Alpine Meadow
model selection 1
## Error in if (any(nEQ <- vNms != make.names(vNms))) vNms[nEQ] <- paste0("`", :
## missing value where TRUE/FALSE needed
## Error in if (any(nEQ <- vNms != make.names(vNms))) vNms[nEQ] <- paste0("`", :
## missing value where TRUE/FALSE needed
## model AIC
## 1 1 NA
## 2 2 NA## Warning in min(AIC1_M313$AIC, na.rm = T): no non-missing arguments to min;
## returning Inf## Error in h(simpleError(msg, call)) :
## error in evaluating the argument 'object' in selecting a method for function 'summary': object 'nls_M313.' not foundsummary
- simple model: does not fit
- alpha model: does not fit
model selection 2
summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
predict and plot
## [1] "cannot plot data with prediction"plotting 2
M331 - Southern Rocky Mountain Steppe - Open Woodland - Coniferous Forest - Alpine Meadow
model selection 1
## Error in if (any(nEQ <- vNms != make.names(vNms))) vNms[nEQ] <- paste0("`", :
## missing value where TRUE/FALSE needed
## Error in if (any(nEQ <- vNms != make.names(vNms))) vNms[nEQ] <- paste0("`", :
## missing value where TRUE/FALSE needed
## model AIC
## 1 1 NA
## 2 2 NA## Warning in min(AIC1_M331$AIC, na.rm = T): no non-missing arguments to min;
## returning Inf## Error in h(simpleError(msg, call)) :
## error in evaluating the argument 'object' in selecting a method for function 'summary': object 'nls_M331.' not foundsummary
- simple model: does not fit
- alpha model: does not fit
model selection 2
summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
predict and plot
## [1] "cannot plot data with prediction"plotting 2
M332 - Middle Rocky Mountain Steppe - Coniferous Forest - Alpine Meadow
model selection 1
summary
- simple model: does not fit
- alpha model: does not fit
model selection 2
summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
predict and plot
## [1] "cannot plot data with prediction"plotting 2
M333 - Northern Rocky Mountain Steppe - Coniferous Forest - Alpine Meadow
model selection 1
summary
- simple model: does not fit
- alpha model: does not fit
model selection 2
summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
predict and plot
## [1] "cannot plot data with prediction"plotting 2
M334 - Black Hills Coniferous Forest
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 215 222.36
## 2 214 201.87 1 20.497 21.729 5.521e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 662.1249
## 2 2 643.0428##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 -
## alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 0.09638 1.54157 0.063 0.95021
## alpha 0.90186 0.16677 5.408 1.7e-07 ***
## A 2.61571 0.91872 2.847 0.00484 **
## k 39.67674 14.90211 2.662 0.00835 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9712 on 214 degrees of freedom
##
## Number of iterations to convergence: 8
## Achieved convergence tolerance: 6.091e-06
## (88 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
model selection 2
## Error in nls(get(paste("fg_", Mod.Sel1, "a", sep = "")), data = G_M334, :
## object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "b", sep = "")), data = G_M334, :
## object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "c", sep = "")), data = G_M334, :
## object 'ge.fit' not found
## model AIC
## 1 2 643.0428
## 2 2a NA
## 3 2b NA
## 4 2c NA##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 -
## alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 0.09638 1.54157 0.063 0.95021
## alpha 0.90186 0.16677 5.408 1.7e-07 ***
## A 2.61571 0.91872 2.847 0.00484 **
## k 39.67674 14.90211 2.662 0.00835 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9712 on 214 degrees of freedom
##
## Number of iterations to convergence: 8
## Achieved convergence tolerance: 6.091e-06
## (88 observations deleted due to missingness)summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
predict and plot
## Warning: Removed 47 rows containing missing values (`geom_point()`).## Warning: Removed 1264 rows containing missing values (`geom_line()`).
plotting 2
M341 - Nevada-Utah Mountains Semi-Desert - Coniferous Forest - Alpine Meadow
model selection 1
summary
- simple model: does not fit
- alpha model: does not fit
model selection 2
summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
predict and plot
## [1] "cannot plot data with prediction"plotting 2
Fitted parameters
Best / selected models by ecoprovince
| Code | Ecoregion | Sel.Mod |
|---|---|---|
| 211 | Northeastern Mixed Forest | 2 |
| 212 | Laurentian Mixed Forest | 2 |
| 221 | Eastern Broadleaf Forest | 2 |
| 222 | Midwest Broadleaf Forest | 2 |
| 223 | Central Interior Broadleaf Forest | 2 |
| 231 | Southeastern Mixed Forest | 2 |
| 232 | Outer Coastal Plain Mixed Forest | 2 |
| 234 | Lower Mississippi Riverine Forest | 2 |
| 242 | Pacific Lowland Mixed Forest | NA |
| 251 | Prairie Parkland (Temperate) | 2 |
| 255 | Prairie Parkland (Subtropical) | NA |
| 261 | California Coastal Chaparral Forest and Shrub | NA |
| 262 | California Dry Steppe | NA |
| 263 | California Coastal Steppe - Mixed Forest and Redwood Forest | NA |
| 313 | Colorado Plateau Semi-Desert | NA |
| 315 | Southwest Plateau and Plains Dry Steppe and Shrub | NA |
| 321 | Chihuahuan Semi-Desert | NA |
| 322 | American Semidesert and Desert | NA |
| 331 | Great Plains/Palouse Dry Steppe | NA |
| 332 | Great Plains Steppe | 2 |
| 341 | Intermountain Semi-Desert and Desert | NA |
| 342 | Intermountain Semi-Desert | NA |
| 411 | Everglades | NA |
| M211 | Adirondack-New England Mixed forest - Coniferous Forest - Alpine Meadow | 2 |
| M221 | Central Appalachian Broadleaf Forest - Coniferous Forest - Meadow | 2 |
| M223 | Ozark Broadleaf Forest Meadow | 2 |
| M231 | Ouachita Mixed Forest | 2 |
| M242 | Cascade Mixed Forest | NA |
| M261 | Sierran Steppe - Mixed Forest - Coniferous Forest - Alpine Meadow | 2 |
| M262 | California Coastal Range Coniferous Forest - Open Woodland - Shrub - Meadow | NA |
| M313 | Arizona-New Mexico Mountains Semi-Desert - Open Woodland - Coniferous Forest - Alpine Meadow | NA |
| M331 | Southern Rocky Mountain Steppe - Open Woodland - Coniferous Forest - Alpine Meadow | NA |
| M332 | Middle Rocky Mountain Steppe - Coniferous Forest - Alpine Meadow | NA |
| M333 | Northern Rocky Mountain Steppe - Coniferous Forest - Alpine Meadow | NA |
| M334 | Black Hills Coniferous Forest | 2 |
| M341 | Nevada-Utah Mountains Semi-Desert - Coniferous Forest - Alpine Meadow | NA |
table by ecoprovince
| Code | Ecoregion | region | n.obs | n.plots | tau | tau.variance | tau.2.5 | tau.97.5 | alpha | alpha.variance | alpha.2.5 | alpha.97.5 | A | A.2.5 | A.97.5 | k | k.2.5 | k.97.5 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 211 | Northeastern Mixed Forest | east | 4838 | 2419 | 0.1164475 | 0.0282348 | -0.2129723 | 0.4458674 | 0.6305879 | 0.0016637 | 0.5506233 | 0.7105525 | 3.597949 | 3.3484006 | 3.847497 | 7.385702 | 5.8476167 | 8.923787 |
| 212 | Laurentian Mixed Forest | east | 12962 | 6481 | 1.2363568 | 0.0447831 | 0.8215376 | 1.6511761 | 0.8120482 | 0.0008156 | 0.7560686 | 0.8680278 | 2.543365 | 2.3634191 | 2.723312 | 10.179028 | 9.0129689 | 11.345086 |
| 221 | Eastern Broadleaf Forest | east | 5446 | 2723 | -0.5929833 | 0.0206033 | -0.8743764 | -0.3115902 | 0.6942633 | 0.0020776 | 0.6049058 | 0.7836208 | 4.897438 | 4.5442146 | 5.250662 | 14.727831 | 11.1217236 | 18.333938 |
| 222 | Midwest Broadleaf Forest | east | 3552 | 1776 | 0.2070426 | 0.0739466 | -0.3261685 | 0.7402537 | 0.8407329 | 0.0027899 | 0.7371630 | 0.9443028 | 4.207700 | 3.7306565 | 4.684743 | 20.430915 | 16.4142872 | 24.447543 |
| 223 | Central Interior Broadleaf Forest | east | 6388 | 3194 | -0.8355260 | 0.0150504 | -1.0760297 | -0.5950222 | 0.6417519 | 0.0025077 | 0.5435803 | 0.7399234 | 5.200768 | 4.8024420 | 5.599095 | 30.290857 | 24.1075514 | 36.474162 |
| 231 | Southeastern Mixed Forest | east | 7790 | 3895 | 1.5672513 | 0.0444275 | 1.1540679 | 1.9804347 | 0.8719574 | 0.0007426 | 0.8185403 | 0.9253745 | 3.834827 | 3.5870605 | 4.082594 | 1.357211 | 0.9247348 | 1.789687 |
| 232 | Outer Coastal Plain Mixed Forest | east | 7940 | 3970 | 1.1253118 | 0.0479741 | 0.6959541 | 1.5546695 | 0.8682073 | 0.0008366 | 0.8115100 | 0.9249047 | 4.011815 | 3.7121665 | 4.311463 | 5.608382 | 4.5415246 | 6.675239 |
| 234 | Lower Mississippi Riverine Forest | east | 830 | 415 | 0.5317652 | 0.4742099 | -0.8199757 | 1.8835060 | 0.7876664 | 0.0098065 | 0.5932802 | 0.9820527 | 4.119109 | 3.0208746 | 5.217344 | 2.019944 | -0.4116854 | 4.451573 |
| 242 | Pacific Lowland Mixed Forest | west | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 251 | Prairie Parkland (Temperate) | east | 1392 | 696 | 0.0182613 | 0.2899913 | -1.0385063 | 1.0750289 | 0.4550613 | 0.0233662 | 0.1550890 | 0.7550335 | 3.319189 | 2.5359619 | 4.102416 | 10.405567 | 3.7090586 | 17.102076 |
| 255 | Prairie Parkland (Subtropical) | east | 444 | 222 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 261 | California Coastal Chaparral Forest and Shrub | west | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 262 | California Dry Steppe | west | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 263 | California Coastal Steppe - Mixed Forest and Redwood Forest | west | 4 | 2 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 313 | Colorado Plateau Semi-Desert | west | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 315 | Southwest Plateau and Plains Dry Steppe and Shrub | west | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 321 | Chihuahuan Semi-Desert | west | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 322 | American Semidesert and Desert | west | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 331 | Great Plains/Palouse Dry Steppe | west | 118 | 59 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 332 | Great Plains Steppe | west | 154 | 77 | 1.5868390 | 6.3786373 | -3.4080136 | 6.5816916 | 0.5937701 | NA | -0.0069897 | 1.1945298 | 2.984765 | 0.3932120 | 5.576318 | 58.321034 | 10.3693915 | 106.272676 |
| 341 | Intermountain Semi-Desert and Desert | west | 4 | 2 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 342 | Intermountain Semi-Desert | west | 2 | 1 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 411 | Everglades | east | 66 | 33 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| M211 | Adirondack-New England Mixed forest - Coniferous Forest - Alpine Meadow | east | 5108 | 2554 | 0.9046325 | 0.0527318 | 0.4544509 | 1.3548140 | 0.6410909 | 0.0012094 | 0.5729141 | 0.7092676 | 2.913018 | 2.6747205 | 3.151316 | 2.855283 | 1.9072259 | 3.803340 |
| M221 | Central Appalachian Broadleaf Forest - Coniferous Forest - Meadow | east | 5186 | 2593 | 0.4574666 | 0.0500797 | 0.0187531 | 0.8961800 | 0.8004319 | 0.0048007 | 0.6646004 | 0.9362633 | 3.805030 | 3.4695975 | 4.140463 | 8.319741 | 4.6485795 | 11.990902 |
| M223 | Ozark Broadleaf Forest Meadow | east | 602 | 301 | 3.0417063 | 2.7955752 | -0.2420279 | 6.3254406 | 0.9604446 | 0.0428776 | 0.5537696 | 1.3671195 | 1.902610 | 1.0471776 | 2.758043 | 9.271931 | -2.9695516 | 21.513413 |
| M231 | Ouachita Mixed Forest | east | 680 | 340 | 7.7044298 | 21.1473763 | -1.3250505 | 16.7339102 | 0.9156032 | 0.0243794 | 0.6090219 | 1.2221844 | 1.122126 | 0.2883033 | 1.955949 | 3.448063 | -0.5391759 | 7.435301 |
| M242 | Cascade Mixed Forest | west | 34 | 17 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| M261 | Sierran Steppe - Mixed Forest - Coniferous Forest - Alpine Meadow | west | 330 | 165 | -1.1540760 | 9.7697568 | -7.3272447 | 5.0190927 | 0.7702956 | 0.1235056 | 0.0762154 | 1.4643758 | 9.120498 | -9.8460118 | 28.087007 | 216.510553 | -78.8927296 | 511.913836 |
| M262 | California Coastal Range Coniferous Forest - Open Woodland - Shrub - Meadow | west | 8 | 4 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| M313 | Arizona-New Mexico Mountains Semi-Desert - Open Woodland - Coniferous Forest - Alpine Meadow | west | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| M331 | Southern Rocky Mountain Steppe - Open Woodland - Coniferous Forest - Alpine Meadow | west | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| M332 | Middle Rocky Mountain Steppe - Coniferous Forest - Alpine Meadow | west | 20 | 10 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| M333 | Northern Rocky Mountain Steppe - Coniferous Forest - Alpine Meadow | west | 22 | 11 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| M334 | Black Hills Coniferous Forest | west | 306 | 153 | 0.0963763 | 2.3764227 | -2.9422198 | 3.1349725 | 0.9018564 | 0.0278121 | 0.5731348 | 1.2305780 | 2.615705 | 0.8048144 | 4.426596 | 39.676735 | 10.3030225 | 69.050449 |
| M341 | Nevada-Utah Mountains Semi-Desert - Coniferous Forest - Alpine Meadow | west | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
plot tau
map
## OGR data source with driver: ESRI Shapefile
## Source: "C:\Users\hogan.jaaron\Dropbox\FIA_R\Mapping\S_USA.EcoMapProvinces\S_USA.EcoMapProvinces.shp", layer: "S_USA.EcoMapProvinces"
## with 37 features
## It has 17 fields
## Integer64 fields read as strings: PROVINCE_ PROVINCE_I## Warning: package 'ggnewscale' was built under R version 4.2.1## Warning: `aes_string()` was deprecated in ggplot2 3.0.0.
## ℹ Please use tidy evaluation ideoms with `aes()`## Warning: The `size` argument of `element_line()` is deprecated as of ggplot2 3.4.0.
## ℹ Please use the `linewidth` argument instead.## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family not
## found in Windows font database
## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family not
## found in Windows font database## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
plot alpha (biomass growth compensation effect)
plot A (asymptote of forest biomass growth in Mg/ha/yr)
## Warning: Removed 20 rows containing missing values (`geom_point()`).
plot k (stand biomass at half biomss G in Mg/ha)
## Warning: Removed 20 rows containing missing values (`geom_point()`).
Analysis 2: No-harvest
211 - Northeastern Mixed Forest
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 4721 4916.7
## 2 4720 4881.9 1 34.848 33.693 6.876e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 19060.92
## 2 2 19029.32##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 -
## alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 0.25718 0.22186 1.159 0.246
## alpha 0.54983 0.09075 6.059 1.48e-09 ***
## A 3.42289 0.15250 22.446 < 2e-16 ***
## k 5.73464 0.70009 8.191 3.30e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.017 on 4720 degrees of freedom
##
## Number of iterations to convergence: 7
## Achieved convergence tolerance: 6.416e-07
## (48 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
model selection 2
## Error in nls(get(paste("fg_", Mod.Sel1, "a", sep = "")), data = G_211, :
## object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "b", sep = "")), data = G_211, :
## object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "c", sep = "")), data = G_211, :
## object 'ge.fit' not found
## model AIC
## 1 2 19029.32
## 2 2a NA
## 3 2b NA
## 4 2c NA##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 -
## alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 0.25718 0.22186 1.159 0.246
## alpha 0.54983 0.09075 6.059 1.48e-09 ***
## A 3.42289 0.15250 22.446 < 2e-16 ***
## k 5.73464 0.70009 8.191 3.30e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.017 on 4720 degrees of freedom
##
## Number of iterations to convergence: 7
## Achieved convergence tolerance: 6.416e-07
## (48 observations deleted due to missingness)summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
predict and plot
## Warning: Removed 24 rows containing missing values (`geom_point()`).## Warning: Removed 1038 rows containing missing values (`geom_line()`).
plotting 2
212 - Laurentian Mixed Forest
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 14328 15511
## 2 14327 15380 1 131.28 122.29 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 53405.21
## 2 2 53285.40##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 -
## alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 0.99405 0.18901 5.259 1.47e-07 ***
## alpha 0.54924 0.04753 11.557 < 2e-16 ***
## A 2.61620 0.08343 31.358 < 2e-16 ***
## k 12.92731 0.63594 20.328 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.036 on 14327 degrees of freedom
##
## Number of iterations to convergence: 6
## Achieved convergence tolerance: 4.266e-06
## (2819 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
model selection 2
## Error in nls(get(paste("fg_", Mod.Sel1, "a", sep = "")), data = G_212, :
## object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "b", sep = "")), data = G_212, :
## object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "c", sep = "")), data = G_212, :
## object 'ge.fit' not found
## model AIC
## 1 2 53285.4
## 2 2a NA
## 3 2b NA
## 4 2c NA##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 -
## alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 0.99405 0.18901 5.259 1.47e-07 ***
## alpha 0.54924 0.04753 11.557 < 2e-16 ***
## A 2.61620 0.08343 31.358 < 2e-16 ***
## k 12.92731 0.63594 20.328 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.036 on 14327 degrees of freedom
##
## Number of iterations to convergence: 6
## Achieved convergence tolerance: 4.266e-06
## (2819 observations deleted due to missingness)summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
predict and plot
## Warning: Removed 1414 rows containing missing values (`geom_point()`).## Warning: Removed 1031 rows containing missing values (`geom_line()`).
plotting 2
221 - Eastern Broadleaf Forest
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 5740 7961.8
## 2 5739 7865.8 1 96.062 70.088 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 25551.04
## 2 2 25483.33##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 -
## alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau -0.78625 0.14354 -5.478 4.49e-08 ***
## alpha 0.65120 0.07468 8.720 < 2e-16 ***
## A 5.08801 0.19219 26.474 < 2e-16 ***
## k 14.08252 1.72467 8.165 3.91e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.171 on 5739 degrees of freedom
##
## Number of iterations to convergence: 5
## Achieved convergence tolerance: 9.117e-06
## (59 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
model selection 2
## Error in nls(get(paste("fg_", Mod.Sel1, "a", sep = "")), data = G_221, :
## object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "b", sep = "")), data = G_221, :
## object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "c", sep = "")), data = G_221, :
## object 'ge.fit' not found
## model AIC
## 1 2 25483.33
## 2 2a NA
## 3 2b NA
## 4 2c NA##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 -
## alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau -0.78625 0.14354 -5.478 4.49e-08 ***
## alpha 0.65120 0.07468 8.720 < 2e-16 ***
## A 5.08801 0.19219 26.474 < 2e-16 ***
## k 14.08252 1.72467 8.165 3.91e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.171 on 5739 degrees of freedom
##
## Number of iterations to convergence: 5
## Achieved convergence tolerance: 9.117e-06
## (59 observations deleted due to missingness)summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
predict and plot
## Warning: Removed 27 rows containing missing values (`geom_point()`).## Warning: Removed 1036 rows containing missing values (`geom_line()`).
plotting 2
222 - Midwest Broadleaf Forest
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 3605 4558.0
## 2 3604 4485.9 1 72.022 57.862 3.565e-14 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 14997.96
## 2 2 14942.50##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 -
## alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau -0.07417 0.25506 -0.291 0.771
## alpha 0.62537 0.07695 8.126 6.02e-16 ***
## A 4.25283 0.22891 18.579 < 2e-16 ***
## k 18.66572 1.77328 10.526 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.116 on 3604 degrees of freedom
##
## Number of iterations to convergence: 7
## Achieved convergence tolerance: 4.332e-06
## (756 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
model selection 2
## Error in nls(get(paste("fg_", Mod.Sel1, "a", sep = "")), data = G_222, :
## object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "b", sep = "")), data = G_222, :
## object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "c", sep = "")), data = G_222, :
## object 'ge.fit' not found
## model AIC
## 1 2 14942.5
## 2 2a NA
## 3 2b NA
## 4 2c NA##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 -
## alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau -0.07417 0.25506 -0.291 0.771
## alpha 0.62537 0.07695 8.126 6.02e-16 ***
## A 4.25283 0.22891 18.579 < 2e-16 ***
## k 18.66572 1.77328 10.526 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.116 on 3604 degrees of freedom
##
## Number of iterations to convergence: 7
## Achieved convergence tolerance: 4.332e-06
## (756 observations deleted due to missingness)summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
predict and plot
## Warning: Removed 367 rows containing missing values (`geom_point()`).## Warning: Removed 1053 rows containing missing values (`geom_line()`).
plotting 2
223 - Central Interior Broadleaf Forest
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 6522 8588.1
## 2 6521 8541.1 1 47.021 35.9 2.188e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 27316.33
## 2 2 27282.51##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 -
## alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau -0.55231 0.15289 -3.613 0.000305 ***
## alpha 0.50136 0.08087 6.200 6e-10 ***
## A 4.12376 0.16155 25.526 < 2e-16 ***
## k 18.46653 2.16245 8.540 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.144 on 6521 degrees of freedom
##
## Number of iterations to convergence: 18
## Achieved convergence tolerance: 6.422e-06
## (906 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
model selection 2
## Error in nls(get(paste("fg_", Mod.Sel1, "a", sep = "")), data = G_223, :
## object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "b", sep = "")), data = G_223, :
## object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "c", sep = "")), data = G_223, :
## object 'ge.fit' not found
## model AIC
## 1 2 27282.51
## 2 2a NA
## 3 2b NA
## 4 2c NA##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 -
## alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau -0.55231 0.15289 -3.613 0.000305 ***
## alpha 0.50136 0.08087 6.200 6e-10 ***
## A 4.12376 0.16155 25.526 < 2e-16 ***
## k 18.46653 2.16245 8.540 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.144 on 6521 degrees of freedom
##
## Number of iterations to convergence: 18
## Achieved convergence tolerance: 6.422e-06
## (906 observations deleted due to missingness)summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
predict and plot
## Warning: Removed 464 rows containing missing values (`geom_point()`).## Warning: Removed 1146 rows containing missing values (`geom_line()`).
plotting 2
231 - Southeastern Mixed Forest
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 9026 16956
## 2 9025 16691 1 265.44 143.53 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 44911.25
## 2 2 44770.79##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 -
## alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 1.83758 0.23485 7.824 5.67e-15 ***
## alpha 0.71230 0.05663 12.579 < 2e-16 ***
## A 3.59730 0.12348 29.133 < 2e-16 ***
## k 0.90233 0.15077 5.985 2.25e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.36 on 9025 degrees of freedom
##
## Number of iterations to convergence: 8
## Achieved convergence tolerance: 5.034e-06
## (238 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
model selection 2
## Error in nls(get(paste("fg_", Mod.Sel1, "a", sep = "")), data = G_231, :
## object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "b", sep = "")), data = G_231, :
## object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "c", sep = "")), data = G_231, :
## object 'ge.fit' not found
## model AIC
## 1 2 44770.79
## 2 2a NA
## 3 2b NA
## 4 2c NA##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 -
## alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 1.83758 0.23485 7.824 5.67e-15 ***
## alpha 0.71230 0.05663 12.579 < 2e-16 ***
## A 3.59730 0.12348 29.133 < 2e-16 ***
## k 0.90233 0.15077 5.985 2.25e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.36 on 9025 degrees of freedom
##
## Number of iterations to convergence: 8
## Achieved convergence tolerance: 5.034e-06
## (238 observations deleted due to missingness)summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
predict and plot
## Warning: Removed 121 rows containing missing values (`geom_point()`).## Warning: Removed 1017 rows containing missing values (`geom_line()`).
plotting 2
232 - Outer Coastal Plain Mixed Forest
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 9259 21370
## 2 9258 21035 1 334.48 147.21 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 46753.75
## 2 2 46609.64##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 -
## alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 1.31980 0.23966 5.507 3.75e-08 ***
## alpha 0.65151 0.05078 12.829 < 2e-16 ***
## A 3.74027 0.14835 25.213 < 2e-16 ***
## k 5.27952 0.48770 10.825 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.507 on 9258 degrees of freedom
##
## Number of iterations to convergence: 7
## Achieved convergence tolerance: 9.266e-06
## (244 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
model selection 2
## Error in nls(get(paste("fg_", Mod.Sel1, "a", sep = "")), data = G_232, :
## object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "b", sep = "")), data = G_232, :
## object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "c", sep = "")), data = G_232, :
## object 'ge.fit' not found
## model AIC
## 1 2 46609.64
## 2 2a NA
## 3 2b NA
## 4 2c NA##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 -
## alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 1.31980 0.23966 5.507 3.75e-08 ***
## alpha 0.65151 0.05078 12.829 < 2e-16 ***
## A 3.74027 0.14835 25.213 < 2e-16 ***
## k 5.27952 0.48770 10.825 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.507 on 9258 degrees of freedom
##
## Number of iterations to convergence: 7
## Achieved convergence tolerance: 9.266e-06
## (244 observations deleted due to missingness)summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
predict and plot
## Warning: Removed 120 rows containing missing values (`geom_point()`).## Warning: Removed 953 rows containing missing values (`geom_line()`).
plotting 2
234 - Lower Mississippi Riverine Forest
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 1035 2282.8
## 2 1034 2232.8 1 49.969 23.141 1.729e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 5204.095
## 2 2 5183.121##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 -
## alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 0.9863 0.8349 1.181 0.23771
## alpha 0.7380 0.1432 5.155 3.04e-07 ***
## A 3.8313 0.5718 6.701 3.40e-11 ***
## k 4.5706 1.6297 2.805 0.00513 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.469 on 1034 degrees of freedom
##
## Number of iterations to convergence: 7
## Achieved convergence tolerance: 5.062e-06
## (57 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
model selection 2
## Error in nls(get(paste("fg_", Mod.Sel1, "a", sep = "")), data = G_234, :
## object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "b", sep = "")), data = G_234, :
## object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "c", sep = "")), data = G_234, :
## object 'ge.fit' not found
## model AIC
## 1 2 5183.121
## 2 2a NA
## 3 2b NA
## 4 2c NA##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 -
## alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 0.9863 0.8349 1.181 0.23771
## alpha 0.7380 0.1432 5.155 3.04e-07 ***
## A 3.8313 0.5718 6.701 3.40e-11 ***
## k 4.5706 1.6297 2.805 0.00513 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.469 on 1034 degrees of freedom
##
## Number of iterations to convergence: 7
## Achieved convergence tolerance: 5.062e-06
## (57 observations deleted due to missingness)summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
predict and plot
## Warning: Removed 31 rows containing missing values (`geom_point()`).## Warning: Removed 948 rows containing missing values (`geom_line()`).
plotting 2
242 - Pacific Lowland Mixed Forest
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 72 125.62
## 2 71 111.90 1 13.722 8.7068 0.004292 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 394.7529
## 2 2 388.0772##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 -
## alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau -1.3783 1.3545 -1.018 0.31236
## alpha 1.1110 0.3379 3.288 0.00157 **
## A 10.7685 4.3043 2.502 0.01466 *
## k 20.3645 11.7501 1.733 0.08741 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.255 on 71 degrees of freedom
##
## Number of iterations to convergence: 8
## Achieved convergence tolerance: 9.971e-06
## (3 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
model selection 2
## Error in nls(get(paste("fg_", Mod.Sel1, "a", sep = "")), data = G_242, :
## object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "b", sep = "")), data = G_242, :
## object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "c", sep = "")), data = G_242, :
## object 'ge.fit' not found
## model AIC
## 1 2 388.0772
## 2 2a NA
## 3 2b NA
## 4 2c NA##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 -
## alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau -1.3783 1.3545 -1.018 0.31236
## alpha 1.1110 0.3379 3.288 0.00157 **
## A 10.7685 4.3043 2.502 0.01466 *
## k 20.3645 11.7501 1.733 0.08741 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.255 on 71 degrees of freedom
##
## Number of iterations to convergence: 8
## Achieved convergence tolerance: 9.971e-06
## (3 observations deleted due to missingness)summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
predict and plot
## Warning: Removed 1 rows containing missing values (`geom_point()`).## Warning: Removed 725 rows containing missing values (`geom_line()`).
plotting 2
251 - Prairie Parkland (Temperate)
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 1472 2384.1
## 2 1471 2378.2 1 5.9652 3.6897 0.05494 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 6373.760
## 2 2 6372.065##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 -
## alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 0.5535 0.6090 0.909 0.363576
## alpha 0.3273 0.1647 1.987 0.047073 *
## A 2.9823 0.3374 8.838 < 2e-16 ***
## k 12.2340 3.1528 3.880 0.000109 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.271 on 1471 degrees of freedom
##
## Number of iterations to convergence: 5
## Achieved convergence tolerance: 6.437e-06
## (415 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
model selection 2
## Error in nls(get(paste("fg_", Mod.Sel1, "a", sep = "")), data = G_251, :
## object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "b", sep = "")), data = G_251, :
## object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "c", sep = "")), data = G_251, :
## object 'ge.fit' not found
## model AIC
## 1 2 6372.065
## 2 2a NA
## 3 2b NA
## 4 2c NA##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 -
## alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 0.5535 0.6090 0.909 0.363576
## alpha 0.3273 0.1647 1.987 0.047073 *
## A 2.9823 0.3374 8.838 < 2e-16 ***
## k 12.2340 3.1528 3.880 0.000109 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.271 on 1471 degrees of freedom
##
## Number of iterations to convergence: 5
## Achieved convergence tolerance: 6.437e-06
## (415 observations deleted due to missingness)summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
predict and plot
## Warning: Removed 201 rows containing missing values (`geom_point()`).## Warning: Removed 1176 rows containing missing values (`geom_line()`).
plotting 2
255 - Prairie Parkland (Subtropical)
model selection 1
## Error in nls(fg_1, data = G_255, start = c(tau = tau.start, A = A.start, :
## number of iterations exceeded maximum of 50
## Error in nls(fg_2, data = G_255, start = c(tau = tau.start, alpha = alpha.start, :
## number of iterations exceeded maximum of 50
## model AIC
## 1 1 NA
## 2 2 NA## Warning in min(AIC1_255$AIC, na.rm = T): no non-missing arguments to min;
## returning Inf## Error in h(simpleError(msg, call)) :
## error in evaluating the argument 'object' in selecting a method for function 'summary': object 'nls_255.' not foundsummary
- simple model: does not fit
- alpha model: does not fit
model selection 2
summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
predict and plot
## [1] "cannot plot data with prediction"plotting 2
261 - California Coastal Chaparral Forest and Shrub
model selection 1
summary
- simple model: does not fit
- alpha model: does not fit
model selection 2
summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
predict and plot
## [1] "cannot plot data with prediction"plotting 2
262 - California Dry Steppe
model selection 1
summary
- simple model: does not fit
- alpha model: does not fit
model selection 2
summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
predict and plot
## [1] "cannot plot data with prediction"plotting 2
263 - California Coastal Steppe - Mixed Forest and Redwood Forest
model selection 1
summary
- simple model: does not fit
- alpha model: does not fit
model selection 2
summary
add p model: does not fit
add s model: does not fit
add s+p model: does not fit
note: model fit, but fit was funky due to data being sparse
predict and plot
## [1] "cannot plot data with prediction"plotting 2
313 - Colorado Plateau Semi-Desert
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 201 104.550
## 2 200 96.761 1 7.7888 16.099 8.492e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 482.3063
## 2 2 468.5127##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 -
## alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau -1.1219 1.0353 -1.084 0.27983
## alpha 1.2621 0.2658 4.747 3.92e-06 ***
## A 5.2835 1.9528 2.706 0.00741 **
## k 153.4583 53.8954 2.847 0.00487 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.6956 on 200 degrees of freedom
##
## Number of iterations to convergence: 8
## Achieved convergence tolerance: 2.523e-06
## (3 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
model selection 2
## Error in nls(get(paste("fg_", Mod.Sel1, "a", sep = "")), data = G_313, :
## object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "b", sep = "")), data = G_313, :
## object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "c", sep = "")), data = G_313, :
## object 'ge.fit' not found
## model AIC
## 1 2 468.5127
## 2 2a NA
## 3 2b NA
## 4 2c NA##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 -
## alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau -1.1219 1.0353 -1.084 0.27983
## alpha 1.2621 0.2658 4.747 3.92e-06 ***
## A 5.2835 1.9528 2.706 0.00741 **
## k 153.4583 53.8954 2.847 0.00487 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.6956 on 200 degrees of freedom
##
## Number of iterations to convergence: 8
## Achieved convergence tolerance: 2.523e-06
## (3 observations deleted due to missingness)summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
predict and plot
## Warning: Removed 2 rows containing missing values (`geom_point()`).## Warning: Removed 1103 rows containing missing values (`geom_line()`).
plotting 2
315 - Southwest Plateau and Plains Dry Steppe and Shrub
model selection 1
summary
- simple model: does not fit
- alpha model: does not fit
model selection 2
summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
predict and plot
## [1] "cannot plot data with prediction"plotting 2
321 - Chihuahuan Semi-Desert
model selection 1
summary
- simple model: does not fit
- alpha model: does not fit
model selection 2
summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
predict and plot
## [1] "cannot plot data with prediction"plotting 2
322 - American Semidesert and Desert
model selection 1
summary
- simple model: does not fit
- alpha model: does not fit
model selection 2
summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
predict and plot
## [1] "cannot plot data with prediction"plotting 2
331 - Great Plains/Palouse Dry Steppe
model selection 1
## Error in nls(fg_1, data = G_331, start = c(tau = tau.start, A = A.start, :
## number of iterations exceeded maximum of 50
## Error in nls(fg_2, data = G_331, start = c(tau = tau.start, alpha = alpha.start, :
## number of iterations exceeded maximum of 50
## model AIC
## 1 1 NA
## 2 2 NA## Warning in min(AIC1_331$AIC, na.rm = T): no non-missing arguments to min;
## returning Inf## Error in h(simpleError(msg, call)) :
## error in evaluating the argument 'object' in selecting a method for function 'summary': object 'nls_331.' not foundsummary
- simple model: does not fit
- alpha model: does not fit
model selection 2
summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
predict and plot
## [1] "cannot plot data with prediction"plotting 2
332 - Great Plains Steppe
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 182 156.41
## 2 181 153.12 1 3.2911 3.8903 0.05009 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 622.4346
## 2 2 620.5005##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 -
## alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 0.1519 1.3492 0.113 0.91046
## alpha 0.5797 0.2682 2.161 0.03197 *
## A 4.2499 1.2855 3.306 0.00114 **
## k 55.1667 18.0272 3.060 0.00255 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9198 on 181 degrees of freedom
##
## Number of iterations to convergence: 6
## Achieved convergence tolerance: 2.519e-06
## (36 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
model selection 2
## Error in nls(get(paste("fg_", Mod.Sel1, "a", sep = "")), data = G_332, :
## object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "b", sep = "")), data = G_332, :
## object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "c", sep = "")), data = G_332, :
## object 'ge.fit' not found
## model AIC
## 1 2 620.5005
## 2 2a NA
## 3 2b NA
## 4 2c NA##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 -
## alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 0.1519 1.3492 0.113 0.91046
## alpha 0.5797 0.2682 2.161 0.03197 *
## A 4.2499 1.2855 3.306 0.00114 **
## k 55.1667 18.0272 3.060 0.00255 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9198 on 181 degrees of freedom
##
## Number of iterations to convergence: 6
## Achieved convergence tolerance: 2.519e-06
## (36 observations deleted due to missingness)summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
predict and plot
## Warning: Removed 18 rows containing missing values (`geom_point()`).## Warning: Removed 1120 rows containing missing values (`geom_line()`).
plotting 2
341 - Intermountain Semi-desert & Desert
model selection 1
summary
- simple model: does not fit
- alpha model: does not fit
model selection 2
summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
predict and plot
## [1] "cannot plot data with prediction"plotting 2
342 - Intermountain Semi-Desert
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 107 80.765
## 2 106 72.925 1 7.8396 11.395 0.00103 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 304.5980
## 2 2 295.3662##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 -
## alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 2.1411 5.8542 0.366 0.71529
## alpha 1.0909 0.2798 3.899 0.00017 ***
## A 3.3027 2.8413 1.162 0.24770
## k 86.3803 35.2633 2.450 0.01594 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8294 on 106 degrees of freedom
##
## Number of iterations to convergence: 6
## Achieved convergence tolerance: 9.792e-06
## (9 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
model selection 2
## Error in nls(get(paste("fg_", Mod.Sel1, "a", sep = "")), data = G_342, :
## object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "b", sep = "")), data = G_342, :
## object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "c", sep = "")), data = G_342, :
## object 'ge.fit' not found
## model AIC
## 1 2 295.3662
## 2 2a NA
## 3 2b NA
## 4 2c NA##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 -
## alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 2.1411 5.8542 0.366 0.71529
## alpha 1.0909 0.2798 3.899 0.00017 ***
## A 3.3027 2.8413 1.162 0.24770
## k 86.3803 35.2633 2.450 0.01594 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8294 on 106 degrees of freedom
##
## Number of iterations to convergence: 6
## Achieved convergence tolerance: 9.792e-06
## (9 observations deleted due to missingness)summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
predict and plot
## Warning: Removed 7 rows containing missing values (`geom_point()`).## Warning: Removed 1241 rows containing missing values (`geom_line()`).
plotting 2
411 - Everglades
model selection 1
summary
- simple model: does not fit
- alpha model: does not fit
model selection 2
summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
predict and plot
## [1] "cannot plot data with prediction"plotting 2
M211 - Adirondack-New England Mixed forest - Coniferous Forest - Alpine Meadow
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 4815 3872.9
## 2 4814 3848.1 1 24.79 31.012 2.704e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 18198.99
## 2 2 18170.05##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 -
## alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 0.95347 0.26715 3.569 0.000362 ***
## alpha 0.48590 0.08435 5.761 8.90e-09 ***
## A 2.83465 0.13698 20.693 < 2e-16 ***
## k 2.03851 0.40996 4.972 6.84e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8941 on 4814 degrees of freedom
##
## Number of iterations to convergence: 7
## Achieved convergence tolerance: 1.751e-06
## (20 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
model selection 2
## Error in nls(get(paste("fg_", Mod.Sel1, "a", sep = "")), data = G_M211, :
## object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "b", sep = "")), data = G_M211, :
## object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "c", sep = "")), data = G_M211, :
## object 'ge.fit' not found
## model AIC
## 1 2 18170.05
## 2 2a NA
## 3 2b NA
## 4 2c NA##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 -
## alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 0.95347 0.26715 3.569 0.000362 ***
## alpha 0.48590 0.08435 5.761 8.90e-09 ***
## A 2.83465 0.13698 20.693 < 2e-16 ***
## k 2.03851 0.40996 4.972 6.84e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8941 on 4814 degrees of freedom
##
## Number of iterations to convergence: 7
## Achieved convergence tolerance: 1.751e-06
## (20 observations deleted due to missingness)summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
predict and plot
## Warning: Removed 14 rows containing missing values (`geom_point()`).## Warning: Removed 1108 rows containing missing values (`geom_line()`).
plotting 2
M221 - Eastern Broadleaf Forest
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 7145 13098
## 2 7144 12995 1 103.05 56.652 5.84e-14 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 33826.78
## 2 2 33772.32##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 -
## alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 0.52729 0.21178 2.490 0.0128 *
## alpha 0.70730 0.09049 7.816 6.21e-15 ***
## A 3.65539 0.15155 24.120 < 2e-16 ***
## k 7.61802 1.53132 4.975 6.68e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.349 on 7144 degrees of freedom
##
## Number of iterations to convergence: 10
## Achieved convergence tolerance: 6.416e-06
## (53 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
model selection 2
## Error in nls(get(paste("fg_", Mod.Sel1, "a", sep = "")), data = G_M221, :
## object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "b", sep = "")), data = G_M221, :
## object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "c", sep = "")), data = G_M221, :
## object 'ge.fit' not found
## model AIC
## 1 2 33772.32
## 2 2a NA
## 3 2b NA
## 4 2c NA##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 -
## alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 0.52729 0.21178 2.490 0.0128 *
## alpha 0.70730 0.09049 7.816 6.21e-15 ***
## A 3.65539 0.15155 24.120 < 2e-16 ***
## k 7.61802 1.53132 4.975 6.68e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.349 on 7144 degrees of freedom
##
## Number of iterations to convergence: 10
## Achieved convergence tolerance: 6.416e-06
## (53 observations deleted due to missingness)summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
predict and plot
## Warning: Removed 28 rows containing missing values (`geom_point()`).## Warning: Removed 982 rows containing missing values (`geom_line()`).
plotting 2
M223 - Ozark Broadleaf Forest Meadow
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 746 1140.6
## 2 745 1128.5 1 12.06 7.9616 0.004905 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 3158.420
## 2 2 3152.459##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 -
## alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 2.5787 1.5196 1.697 0.09013 .
## alpha 0.8074 0.2698 2.993 0.00286 **
## A 1.9230 0.4245 4.530 6.88e-06 ***
## k 7.9486 6.2151 1.279 0.20133
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.231 on 745 degrees of freedom
##
## Number of iterations to convergence: 19
## Achieved convergence tolerance: 6.392e-06
## (6 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
model selection 2
## Error in nls(get(paste("fg_", Mod.Sel1, "a", sep = "")), data = G_M223, :
## object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "b", sep = "")), data = G_M223, :
## object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "c", sep = "")), data = G_M223, :
## object 'ge.fit' not found
## model AIC
## 1 2 3152.459
## 2 2a NA
## 3 2b NA
## 4 2c NA##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 -
## alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 2.5787 1.5196 1.697 0.09013 .
## alpha 0.8074 0.2698 2.993 0.00286 **
## A 1.9230 0.4245 4.530 6.88e-06 ***
## k 7.9486 6.2151 1.279 0.20133
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.231 on 745 degrees of freedom
##
## Number of iterations to convergence: 19
## Achieved convergence tolerance: 6.392e-06
## (6 observations deleted due to missingness)summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
predict and plot
## Warning: Removed 4 rows containing missing values (`geom_point()`).## Warning: Removed 1175 rows containing missing values (`geom_line()`).
plotting 2
M231 - Ouachita Mixed Forest
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 826 1189.6
## 2 825 1182.1 1 7.5756 5.2873 0.02173 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 3499.462
## 2 2 3496.166##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 -
## alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 5.1519 2.9120 1.769 0.077227 .
## alpha 0.6308 0.2634 2.395 0.016853 *
## A 1.3964 0.4191 3.332 0.000901 ***
## k 2.2591 1.3416 1.684 0.092579 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.197 on 825 degrees of freedom
##
## Number of iterations to convergence: 15
## Achieved convergence tolerance: 6.576e-06
## (11 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
model selection 2
## Error in nls(get(paste("fg_", Mod.Sel1, "a", sep = "")), data = G_M231, :
## object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "b", sep = "")), data = G_M231, :
## object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "c", sep = "")), data = G_M231, :
## object 'ge.fit' not found
## model AIC
## 1 2 3496.166
## 2 2a NA
## 3 2b NA
## 4 2c NA##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 -
## alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 5.1519 2.9120 1.769 0.077227 .
## alpha 0.6308 0.2634 2.395 0.016853 *
## A 1.3964 0.4191 3.332 0.000901 ***
## k 2.2591 1.3416 1.684 0.092579 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.197 on 825 degrees of freedom
##
## Number of iterations to convergence: 15
## Achieved convergence tolerance: 6.576e-06
## (11 observations deleted due to missingness)summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
predict and plot
## Warning: Removed 1 rows containing missing values (`geom_point()`).## Warning: Removed 1218 rows containing missing values (`geom_line()`).
plotting 2
M242 - Cascade Mixed Forest
model selection 1
summary
- simple model: does not fit
- alpha model: does not fit
model selection 2
summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
predict and plot
## [1] "cannot plot data with prediction"plotting 2
M261 - Sierran Steppe - Mixed Forest - Coniferous Forest - Alpine Meadow
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 1603 3802.8
## 2 1602 3719.1 1 83.683 36.046 2.379e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 7718.795
## 2 2 7685.060##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 -
## alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau -1.8923 0.3279 -5.771 9.43e-09 ***
## alpha 0.7975 0.1230 6.483 1.20e-10 ***
## A 14.1504 1.7044 8.302 < 2e-16 ***
## k 193.9490 25.3297 7.657 3.27e-14 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.524 on 1602 degrees of freedom
##
## Number of iterations to convergence: 6
## Achieved convergence tolerance: 2.914e-06
## (273 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
model selection 2
## Error in nls(get(paste("fg_", Mod.Sel1, "a", sep = "")), data = G_M261, :
## object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "b", sep = "")), data = G_M261, :
## object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "c", sep = "")), data = G_M261, :
## object 'ge.fit' not found
## model AIC
## 1 2 7685.06
## 2 2a NA
## 3 2b NA
## 4 2c NA##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 -
## alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau -1.8923 0.3279 -5.771 9.43e-09 ***
## alpha 0.7975 0.1230 6.483 1.20e-10 ***
## A 14.1504 1.7044 8.302 < 2e-16 ***
## k 193.9490 25.3297 7.657 3.27e-14 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.524 on 1602 degrees of freedom
##
## Number of iterations to convergence: 6
## Achieved convergence tolerance: 2.914e-06
## (273 observations deleted due to missingness)summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
predict and plot
## Warning: Removed 135 rows containing missing values (`geom_point()`).
plotting 2
M262 - Califormia Coastal Range = Coniferous Forest - Open woodland Shrub Meadow
model selection 1
summary
- simple model: does not fit
- alpha model: does not fit
model selection 2
summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
predict and plot
## [1] "cannot plot data with prediction"plotting 2
M313 - Arizona-New Mexico Mountains Semi-Desert - Open Woodland - Coniferous Forest - Alpine Meadow
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 336 159.99
## 2 335 142.37 1 17.619 41.459 4.175e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 799.7921
## 2 2 762.2385##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 -
## alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau -2.3762 0.2768 -8.585 3.47e-16 ***
## alpha 0.8155 0.1125 7.250 2.91e-12 ***
## A 10.4659 1.9945 5.247 2.74e-07 ***
## k 164.2182 40.3132 4.074 5.78e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.6519 on 335 degrees of freedom
##
## Number of iterations to convergence: 7
## Achieved convergence tolerance: 4.896e-06
## (1 observation deleted due to missingness)summary
- simple model: fits
- alpha model: fits
model selection 2
## Error in nls(get(paste("fg_", Mod.Sel1, "a", sep = "")), data = G_M313, :
## object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "b", sep = "")), data = G_M313, :
## object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "c", sep = "")), data = G_M313, :
## object 'ge.fit' not found
## model AIC
## 1 2 762.2385
## 2 2a NA
## 3 2b NA
## 4 2c NA##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 -
## alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau -2.3762 0.2768 -8.585 3.47e-16 ***
## alpha 0.8155 0.1125 7.250 2.91e-12 ***
## A 10.4659 1.9945 5.247 2.74e-07 ***
## k 164.2182 40.3132 4.074 5.78e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.6519 on 335 degrees of freedom
##
## Number of iterations to convergence: 7
## Achieved convergence tolerance: 4.896e-06
## (1 observation deleted due to missingness)summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
predict and plot
## Warning: Removed 1183 rows containing missing values (`geom_line()`).
plotting 2
M331 - Southern Rocky Mountain Steppe - Open Woodland - Coniferous Forest - Alpine Meadow
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 1694 1569.2
## 2 1693 1477.2 1 92.006 105.45 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 4878.014
## 2 2 4777.476##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 -
## alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau -0.82833 0.57146 -1.449 0.147
## alpha 0.70710 0.05849 12.088 < 2e-16 ***
## A 2.51159 0.39307 6.390 2.14e-10 ***
## k 36.74020 6.18642 5.939 3.48e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9341 on 1693 degrees of freedom
##
## Number of iterations to convergence: 9
## Achieved convergence tolerance: 3.496e-06
## (21 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
model selection 2
## Error in nls(get(paste("fg_", Mod.Sel1, "a", sep = "")), data = G_M331, :
## object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "b", sep = "")), data = G_M331, :
## object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "c", sep = "")), data = G_M331, :
## object 'ge.fit' not found
## model AIC
## 1 2 4777.476
## 2 2a NA
## 3 2b NA
## 4 2c NA##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 -
## alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau -0.82833 0.57146 -1.449 0.147
## alpha 0.70710 0.05849 12.088 < 2e-16 ***
## A 2.51159 0.39307 6.390 2.14e-10 ***
## k 36.74020 6.18642 5.939 3.48e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9341 on 1693 degrees of freedom
##
## Number of iterations to convergence: 9
## Achieved convergence tolerance: 3.496e-06
## (21 observations deleted due to missingness)summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
predict and plot
## Warning: Removed 12 rows containing missing values (`geom_point()`).## Warning: Removed 1091 rows containing missing values (`geom_line()`).
plotting 2
M332 - Middle Rocky Mountain Steppe - Coniferous Forest - Alpine Meadow
model selection 1
summary
- simple model: does not fit
- alpha model: does not fit
model selection 2
summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
predict and plot
## [1] "cannot plot data with prediction"plotting 2
M333 - Northern Rocky Mountain Steppe - Coniferous Forest - Alpine Meadow
model selection 1
summary
- simple model: does not fit
- alpha model: does not fit
model selection 2
summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
predict and plot
## [1] "cannot plot data with prediction"plotting 2
M334 - Black Hills Coniferous Forest
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 242 209.84
## 2 241 200.31 1 9.5337 11.47 0.0008256 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 700.2602
## 2 2 690.8684##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 -
## alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 1.5146 2.4868 0.609 0.543054
## alpha 0.8217 0.2258 3.639 0.000335 ***
## A 1.6348 0.6583 2.484 0.013689 *
## k 27.2124 9.9115 2.746 0.006496 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9117 on 241 degrees of freedom
##
## Number of iterations to convergence: 6
## Achieved convergence tolerance: 6.35e-06
## (71 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
model selection 2
## Error in nls(get(paste("fg_", Mod.Sel1, "a", sep = "")), data = G_M334, :
## object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "b", sep = "")), data = G_M334, :
## object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "c", sep = "")), data = G_M334, :
## object 'ge.fit' not found
## model AIC
## 1 2 690.8684
## 2 2a NA
## 3 2b NA
## 4 2c NA##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 -
## alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 1.5146 2.4868 0.609 0.543054
## alpha 0.8217 0.2258 3.639 0.000335 ***
## A 1.6348 0.6583 2.484 0.013689 *
## k 27.2124 9.9115 2.746 0.006496 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9117 on 241 degrees of freedom
##
## Number of iterations to convergence: 6
## Achieved convergence tolerance: 6.35e-06
## (71 observations deleted due to missingness)summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
predict and plot
## Warning: Removed 36 rows containing missing values (`geom_point()`).## Warning: Removed 1264 rows containing missing values (`geom_line()`).
plotting 2
M341 - Nevada-Utah Mountains Semi-Desert - Coniferous Forest - Alpine Meadow
model selection 1
summary
- simple model: does not fit
- alpha model: does not fit
model selection 2
summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
predict and plot
## [1] "cannot plot data with prediction"plotting 2
Fitted parameters
Best / selected models by ecoprovince
| Code | Ecoregion | Sel.Mod |
|---|---|---|
| 211 | Northeastern Mixed Forest | 2 |
| 212 | Laurentian Mixed Forest | 2 |
| 221 | Eastern Broadleaf Forest | 2 |
| 222 | Midwest Broadleaf Forest | 2 |
| 223 | Central Interior Broadleaf Forest | 2 |
| 231 | Southeastern Mixed Forest | 2 |
| 232 | Outer Coastal Plain Mixed Forest | 2 |
| 234 | Lower Mississippi Riverine Forest | 2 |
| 242 | Pacific Lowland Mixed Forest | 2 |
| 251 | Prairie Parkland (Temperate) | 2 |
| 255 | Prairie Parkland (Subtropical) | NA |
| 261 | California Coastal Chaparral Forest and Shrub | NA |
| 262 | California Dry Steppe | NA |
| 263 | California Coastal Steppe - Mixed Forest and Redwood Forest | NA |
| 313 | Colorado Plateau Semi-Desert | 2 |
| 315 | Southwest Plateau and Plains Dry Steppe and Shrub | NA |
| 321 | Chihuahuan Semi-Desert | NA |
| 322 | American Semidesert and Desert | NA |
| 331 | Great Plains/Palouse Dry Steppe | NA |
| 332 | Great Plains Steppe | 2 |
| 341 | Intermountain Semi-Desert and Desert | NA |
| 342 | Intermountain Semi-Desert | 2 |
| 411 | Everglades | NA |
| M211 | Adirondack-New England Mixed forest - Coniferous Forest - Alpine Meadow | 2 |
| M221 | Central Appalachian Broadleaf Forest - Coniferous Forest - Meadow | 2 |
| M223 | Ozark Broadleaf Forest Meadow | 2 |
| M231 | Ouachita Mixed Forest | 2 |
| M242 | Cascade Mixed Forest | NA |
| M261 | Sierran Steppe - Mixed Forest - Coniferous Forest - Alpine Meadow | 2 |
| M262 | California Coastal Range Coniferous Forest - Open Woodland - Shrub - Meadow | NA |
| M313 | Arizona-New Mexico Mountains Semi-Desert - Open Woodland - Coniferous Forest - Alpine Meadow | 2 |
| M331 | Southern Rocky Mountain Steppe - Open Woodland - Coniferous Forest - Alpine Meadow | 2 |
| M332 | Middle Rocky Mountain Steppe - Coniferous Forest - Alpine Meadow | NA |
| M333 | Northern Rocky Mountain Steppe - Coniferous Forest - Alpine Meadow | NA |
| M334 | Black Hills Coniferous Forest | 2 |
| M341 | Nevada-Utah Mountains Semi-Desert - Coniferous Forest - Alpine Meadow | NA |
table by ecoprovince
| Code | Ecoregion | region | n.obs | n.plots | tau | tau.variance | tau.2.5 | tau.97.5 | alpha | alpha.variance | alpha.2.5 | alpha.97.5 | A | A.2.5 | A.97.5 | k | k.2.5 | k.97.5 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 211 | Northeastern Mixed Forest | east | 4838 | 2419 | 0.2571843 | 0.0492240 | -0.1777745 | 0.6921431 | 0.5498301 | 0.0082358 | 0.3719155 | 0.7277448 | 3.422895 | 3.1239310 | 3.721859 | 5.7346375 | 4.3621394 | 7.107136 |
| 212 | Laurentian Mixed Forest | east | 12962 | 6481 | 0.9940460 | 0.0357233 | 0.6235694 | 1.3645226 | 0.5492358 | 0.0022586 | 0.4560805 | 0.6423910 | 2.616201 | 2.4526674 | 2.779735 | 12.9273077 | 11.6807841 | 14.173831 |
| 221 | Eastern Broadleaf Forest | east | 5446 | 2723 | -0.7862498 | 0.0206033 | -1.0676392 | -0.5048605 | 0.6511983 | 0.0055768 | 0.5048017 | 0.7975948 | 5.088010 | 4.7112509 | 5.464768 | 14.0825198 | 10.7015245 | 17.463515 |
| 222 | Midwest Broadleaf Forest | east | 3552 | 1776 | -0.0741662 | 0.0650581 | -0.5742520 | 0.4259196 | 0.6253684 | 0.0059221 | 0.4744888 | 0.7762479 | 4.252835 | 3.8040332 | 4.701637 | 18.6657210 | 15.1889976 | 22.142444 |
| 223 | Central Interior Broadleaf Forest | east | 6388 | 3194 | -0.5523085 | 0.0233741 | -0.8520149 | -0.2526022 | 0.5013643 | 0.0065395 | 0.3428376 | 0.6598909 | 4.123765 | 3.8070702 | 4.440459 | 18.4665284 | 14.2274120 | 22.705645 |
| 231 | Southeastern Mixed Forest | east | 7790 | 3895 | 1.8375843 | 0.0551549 | 1.3772234 | 2.2979452 | 0.7122995 | 0.0032067 | 0.6012968 | 0.8233021 | 3.597299 | 3.3552515 | 3.839347 | 0.9023328 | 0.6067866 | 1.197879 |
| 232 | Outer Coastal Plain Mixed Forest | east | 7940 | 3970 | 1.3198028 | 0.0574385 | 0.8500101 | 1.7895955 | 0.6515053 | 0.0025791 | 0.5519563 | 0.7510542 | 3.740272 | 3.4494812 | 4.031062 | 5.2795174 | 4.3235172 | 6.235518 |
| 234 | Lower Mississippi Riverine Forest | east | 830 | 415 | 0.9863105 | 0.6969788 | -0.6518880 | 2.6245091 | 0.7380360 | 0.0204966 | 0.4571062 | 1.0189658 | 3.831336 | 2.7093954 | 4.953276 | 4.5706428 | 1.3726943 | 7.768591 |
| 242 | Pacific Lowland Mixed Forest | west | 0 | 0 | -1.3782845 | 1.8347993 | -4.0791758 | 1.3226068 | 1.1110433 | 0.1141936 | 0.4372390 | 1.7848476 | 10.768523 | 2.1859512 | 19.351095 | 20.3645230 | -3.0645532 | 43.793599 |
| 251 | Prairie Parkland (Temperate) | east | 1392 | 696 | 0.5534955 | 0.3708856 | -0.6411129 | 1.7481040 | 0.3272821 | NA | 0.0042375 | 0.6503267 | 2.982338 | 2.3204311 | 3.644245 | 12.2339754 | 6.0495953 | 18.418356 |
| 255 | Prairie Parkland (Subtropical) | east | 444 | 222 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 261 | California Coastal Chaparral Forest and Shrub | west | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 262 | California Dry Steppe | west | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 263 | California Coastal Steppe - Mixed Forest and Redwood Forest | west | 4 | 2 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 313 | Colorado Plateau Semi-Desert | west | 0 | 0 | -1.1219292 | 1.0719268 | -3.1635102 | 0.9196518 | 1.2620845 | 0.0706721 | 0.7378711 | 1.7862979 | 5.283480 | 1.4326874 | 9.134272 | 153.4582754 | 47.1820589 | 259.734492 |
| 315 | Southwest Plateau and Plains Dry Steppe and Shrub | west | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 321 | Chihuahuan Semi-Desert | west | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 322 | American Semidesert and Desert | west | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 331 | Great Plains/Palouse Dry Steppe | west | 118 | 59 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 332 | Great Plains Steppe | west | 154 | 77 | 0.1519373 | 1.8204078 | -2.5102953 | 2.8141698 | 0.5797203 | NA | 0.0505029 | 1.1089378 | 4.249859 | 1.7133106 | 6.786408 | 55.1666719 | 19.5962434 | 90.737100 |
| 341 | Intermountain Semi-Desert and Desert | west | 4 | 2 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 342 | Intermountain Semi-Desert | west | 2 | 1 | 2.1411086 | 34.2718076 | -9.4654377 | 13.7476549 | 1.0908503 | 0.0782767 | 0.5361596 | 1.6455410 | 3.302657 | -2.3305330 | 8.935847 | 86.3802797 | 16.4674146 | 156.293145 |
| 411 | Everglades | east | 66 | 33 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| M211 | Adirondack-New England Mixed forest - Coniferous Forest - Alpine Meadow | east | 5108 | 2554 | 0.9534702 | 0.0713666 | 0.4297432 | 1.4771972 | 0.4859013 | 0.0071145 | 0.3205422 | 0.6512604 | 2.834649 | 2.5660997 | 3.103199 | 2.0385096 | 1.2347941 | 2.842225 |
| M221 | Central Appalachian Broadleaf Forest - Coniferous Forest - Meadow | east | 5186 | 2593 | 0.5272910 | 0.0448507 | 0.1121396 | 0.9424423 | 0.7072952 | 0.0081881 | 0.5299120 | 0.8846784 | 3.655387 | 3.3582976 | 3.952476 | 7.6180239 | 4.6161892 | 10.619858 |
| M223 | Ozark Broadleaf Forest Meadow | east | 602 | 301 | 2.5786534 | 2.3092457 | -0.4045942 | 5.5619010 | 0.8074075 | NA | 0.2777849 | 1.3370300 | 1.922964 | 1.0895645 | 2.756363 | 7.9485577 | -4.2526450 | 20.149760 |
| M231 | Ouachita Mixed Forest | east | 680 | 340 | 5.1519387 | 8.4796824 | -0.5638412 | 10.8677186 | 0.6307872 | 0.0693803 | 0.1137715 | 1.1478030 | 1.396358 | 0.5737630 | 2.218953 | 2.2590982 | -0.3742218 | 4.892418 |
| M242 | Cascade Mixed Forest | west | 34 | 17 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| M261 | Sierran Steppe - Mixed Forest - Coniferous Forest - Alpine Meadow | west | 330 | 165 | -1.8922742 | 0.1075033 | -2.5353869 | -1.2491614 | 0.7975471 | 0.0151355 | 0.5562376 | 1.0388567 | 14.150408 | 10.8073327 | 17.493484 | 193.9489866 | 144.2661888 | 243.631784 |
| M262 | California Coastal Range Coniferous Forest - Open Woodland - Shrub - Meadow | west | 8 | 4 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| M313 | Arizona-New Mexico Mountains Semi-Desert - Open Woodland - Coniferous Forest - Alpine Meadow | west | 0 | 0 | -2.3762353 | 0.0766179 | -2.9207193 | -1.8317513 | 0.8155232 | 0.0126547 | 0.5942410 | 1.0368054 | 10.465851 | 6.5424994 | 14.389202 | 164.2181549 | 84.9193244 | 243.516985 |
| M331 | Southern Rocky Mountain Steppe - Open Woodland - Coniferous Forest - Alpine Meadow | west | 0 | 0 | -0.8283286 | 0.3265699 | -1.9491767 | 0.2925194 | 0.7071035 | 0.0034215 | 0.5923755 | 0.8218314 | 2.511590 | 1.7406263 | 3.282553 | 36.7401989 | 24.6063707 | 48.874027 |
| M332 | Middle Rocky Mountain Steppe - Coniferous Forest - Alpine Meadow | west | 20 | 10 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| M333 | Northern Rocky Mountain Steppe - Coniferous Forest - Alpine Meadow | west | 22 | 11 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| M334 | Black Hills Coniferous Forest | west | 306 | 153 | 1.5146289 | 6.1841836 | -3.3840132 | 6.4132710 | 0.8217487 | 0.0510030 | 0.3768795 | 1.2666179 | 1.634831 | 0.3381486 | 2.931513 | 27.2123700 | 7.6882090 | 46.736531 |
| M341 | Nevada-Utah Mountains Semi-Desert - Coniferous Forest - Alpine Meadow | west | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
plot tau
map
## OGR data source with driver: ESRI Shapefile
## Source: "C:\Users\hogan.jaaron\Dropbox\FIA_R\Mapping\S_USA.EcoMapProvinces\S_USA.EcoMapProvinces.shp", layer: "S_USA.EcoMapProvinces"
## with 37 features
## It has 17 fields
## Integer64 fields read as strings: PROVINCE_ PROVINCE_I## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
plot alpha (biomass growth compensation effect)
plot A (asymptote of forest biomass growth in Mg/ha/yr)
## Warning: Removed 15 rows containing missing values (`geom_point()`).
plot k (stand biomass at half biomss G in Mg/ha)
## Warning: Removed 15 rows containing missing values (`geom_point()`).